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Author search results
Likely matches
- Erik G. Learned-Miller
aka: Erik Gundersen Miller, Erik G. Miller
University of Massachusetts Amherst, USA - Katrina Learned
- Rachel E. Learned
- Mount-Learn Wu
- Learnmore Moyo
Venue search results
Likely matches
- International Conference on Medical Image Computing and Computer-Assisted Intervention (MICCAI)
also: International Workshop on Graph Learning in Medical Imaging (GLMI @ MICCAI)
also: International Workshop on Medical Image Learning with Less Labels and Imperfect Data (MIL3ID @ MICCAI)
also: International Workshop on Machine Learning and Medical Engineering for Cardiovascular Health (MLMECH @ MICCAI)
also: International Workshop on Machine Learning in Medical Imaging (MLMI @ MICCAI) - International Conference for High Performance Computing, Networking, Storage and Analysis (SC)
also: Workshop on Deep Learning on Supercomputers (DLS @ SC) - European Conference on Machine Learning and Knowledge Discovery in Databases (PKDD)
also: Machine Learning for Earth ObservatioN Workshop (MACLEAN @ PKDD)
also: Privacy and Security Issues in Data Mining and Machine Learning (PSDML @ PKDD) - International Joint Conference on Artificial Intelligence (IJCAI)
also: Workshop on Machine Learning for Interactive Systems (MLIS @ IJCAI)
also: Workshop on Ontology Learning (OL @ IJCAI) - Machine Learning
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Publication search results
found 493,833 matches
- 2024
- Fragkiskos D. Malliaros:
A Machine Learning Tour in Network Science. University of Paris-Saclay, France, 2024 - Anna Pappa:
Contributions à la création de corpus et de modèles d'apprentissage profond pour les données textuelles multilingues. (Contributions to corpus creation and deep learning models for multilingual textual data). University Paris 8, France, 2024 - Christopher M. Bishop, Hugh Bishop:
Deep Learning - Foundations and Concepts. Springer 2024, ISBN 978-3-031-45467-7, pp. 1-607 - Jinyin Chen, Ximin Zhang, Haibin Zheng:
Attacks, Defenses and Testing for Deep Learning. Springer 2024, ISBN 978-981-97-0424-8, pp. 5-399 - Gerald Friedland:
Information-Driven Machine Learning - Data Science as an Engineering Discipline, 4. Springer 2024, ISBN 978-3-031-39476-8, pp. 1-267 - Jonah Gamba:
Deep Learning Models - A Practical Approach for Hands-On Professionals. Springer 2024, ISBN 978-981-99-9671-1, pp. 1-201 - Rachid Guerraoui, Nirupam Gupta, Rafael Pinot:
Robust Machine Learning - Distributed Methods for Safe AI. Springer 2024, ISBN 978-981-97-0687-7, pp. 1-154 - Francesca Lazzeri, Alexei Robsky:
Machine Learning Governance for Managers. Springer 2024, ISBN 978-3-031-31804-7, pp. 1-108 - Hang Li:
Machine Learning Methods. Springer 2024, ISBN 978-981-99-3916-9, pp. 1-532 - Umberto Michelucci:
Fundamental Mathematical Concepts for Machine Learning in Science. Springer 2024, ISBN 978-3-031-56430-7, pp. 1-242 - Denis Panjuta, Jafar Jabbarzadeh:
Learning C# Through Small Projects. Springer 2024, ISBN 978-3-031-51913-0, pp. 1-391 - Zhixin Qi
, Hongzhi Wang, Zejiao Dong:
Dirty Data Processing for Machine Learning, 2. Springer 2024, ISBN 978-981-99-7656-0, pp. 1-133 - Dhish Kumar Saxena, Sukrit Mittal, Kalyanmoy Deb, Erik D. Goodman:
Machine Learning Assisted Evolutionary Multi- and Many-Objective Optimization. Springer 2024, ISBN 978-981-99-2095-2, pp. 1-239 - Giuseppe Serazzi:
Performance Engineering - Learning Through Applications Using JMT. Springer 2024, ISBN 978-3-031-36762-5, pp. 1-146 - Klaus D. Toennies:
An Introduction to Image Classification - From Designed Models to End-to-End Learning. Springer 2024, ISBN 978-981-99-7881-6, pp. 1-290 - Tom Kohn:
Secrets of Machine Learning - How It Works and What It Means for You. WorldScientific 2024, ISBN 9781800614888, pp. 1-240 - Ahmed Abbas Mohsin Al-Saedi:
Resource-Aware and Personalized Federated Learning via Clustering Analysis. Blekinge Institute of Technology, Karlskrona, Sweden, 2024 - Mahbub Ul Alam:
Advancing Clinical Decision Support Using Machine Learning & the Internet of Medical Things: Enhancing COVID-19 & Early Sepsis Detection. Stockholm University, Sweden, 2024 - Sungtae An:
Weakly Supervised Deep Learning for Human Activity Recognition. Georgia Institute of Technology, Atlanta, GA, USA, 2024 - Muhammad Adeel Azam:
Upper Aero Digestive Tract Cancer Diagnosis using Deep Learning Methods. University of Genoa, Italy, 2024 - Ishaan Batta:
Multimodal Frameworks to Learn Salient Brain Subspaces from Neuroimaging Data. Georgia Institute of Technology, Atlanta, GA, USA, 2024 - Fritz M. Bayer:
Causality in Unsupervised Learning: Methods and Applications in Cancer Genomics. ETH Zurich, Zürich, Switzerland, 2024 - Daniel Biedermann
:
Technological responses to distracting media multitasking in academic learning. Frankfurt University, Germany, 2024 - David Blanco Mulero:
Towards Efficient Robotic Manipulation of Deformable Objects by Learning Dynamics Models and Adaptive Policies. Aalto University, Espoo, Finland, 2024 - Pierre-Paul De Breuck:
Small datasets, big predictions: learning methods for uncertainty-aware modelling of multi-fidelity material properties. Catholic University of Louvain, Louvain-la-Neuve, Belgium, 2024 - Alberto Carlevaro:
Mathematical Methods for eXplainable and Reliable Machine Learning in Trustworthy Artificial Intelligence. University of Genoa, Italy, 2024 - Julio Ivan Davila Carrazco:
Deep learning in data scarcity scenarios. University of Genoa, Italy, 2024 - Evan Downing:
Improving the Understanding of Malware using Machine Learning. Georgia Institute of Technology, Atlanta, GA, USA, 2024 - Jing Du:
Enhancing Personalized Recommendations with Transferable User Representation Learning in Limited Data Contexts. University of New South Wales, Sydney, Australia, 2024 - Alina Dubatovka:
Interpretable and robust Machine Learning models for time-series analysis in cardiology. ETH Zurich, Zürich, Switzerland, 2024
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